model.py
# -*-coding:utf-8-*-
"""author: Zhou Chen
datetime: 2019/6/25 9:10
desc: 实现模型
"""
from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Flatten, Input, ZeroPadding2D, AveragePooling2D, Dense
from keras.layers import add
def Conv2D_BN(x, filters, kernel_size, strides=(1, 1), padding='same', name=None):
if name:
bn_name = name + '_bn'
conv_name = name + '_conv'
else:
bn_name = None
conv_name = None
x = Conv2D(filters, kernel_size, strides=strides, padding=padding, activation='relu', name=conv_name)(x)
x = BatchNormalization(name=bn_name)(x)
return x
def identity_block(input_tensor, filters, kernel_size, strides=(1, 1), is_conv_shortcuts=False):
"""
:param input_tensor:
:param filters:
:param kernel_size:
:param strides:
:param is_conv_shortcuts: 直接连接或者投影连接
:return:
"""
x = Conv2D_BN(input_tensor, filters, kernel_size, strides=strides, padding='same')
x = Conv2D_BN(x, filters, kernel_size, padding='same')
if is_conv_shortcuts:
shortcut = Conv2D_BN(input_tensor, filters, kernel_size, strides=strides, padding='same')
x = add([x, shortcut])
else:
x = add([x, input_tensor])
return x
def bottleneck_block(input_tensor, filters=(64, 64, 256), strides=(1, 1), is_conv_shortcuts=False):
"""
:param input_tensor:
:param filters:
:param strides:
:param is_conv_shortcuts: 直接连接或者投影连接
:return:
"""
filters_1, filters_2, filters_3 = filters
x = Conv2D_BN(input_tensor, filters=filters_1, kernel_size=(1, 1), strides=strides, padding='same')
x = Conv2D_BN(x, filters=filters_2, kernel_size=(3, 3))
x = Conv2D_BN(x, filters=filters_3, kernel_size=(1, 1))
if is_conv_shortcuts:
short_cut = Conv2D_BN(input_tensor, filters=filters_3, kernel_size=(1, 1), strides=strides)
x = add([x, short_cut])
else:
x = add([x, input_tensor])
return x
def ResNet34(input_shape=(224, 224, 3), n_classes=1000):
"""
:param input_shape:
:param n_classes:
:return:
"""
input_layer = Input(shape=input_shape)
x = ZeroPadding2D((3, 3))(input_layer)
# block1
x = Conv2D_BN(x, filters=64, kernel_size=(7, 7), strides=(2, 2), padding='valid')
x = MaxPooling2D(pool_size=(3, 3), strides=2, padding='same')(x)
# block2
x = identity_block(x, filters=64, kernel_size=(3, 3))
x = identity_block(x, filters=64, kernel_size=(3, 3))
x = identity_block(x, filters=64, kernel_size=(3, 3))
# block3
x = identity_block(x, filters=128, kernel_size=(3, 3), strides=(2, 2), is_conv_shortcuts=True)
x = identity_block(x, filters=128, kernel_size=(3, 3))
x = identity_block(x, filters=128, kernel_size=(3, 3))
x = identity_block(x, filters=128, kernel_size=(3, 3))
# block4
x = identity_block(x, filters=256, kernel_size=(3, 3), strides=(2, 2), is_conv_shortcuts=True)
x = identity_block(x, filters=256, kernel_size=(3, 3))
x = identity_block(x, filters=256, kernel_size=(3, 3))
x = identity_block(x, filters=256, kernel_size=(3, 3))
x = identity_block(x, filters=256, kernel_size=(3, 3))
x = identity_block(x, filters=256, kernel_size=(3, 3))
# block5
x = identity_block(x, filters=512, kernel_size=(3, 3), strides=(2, 2), is_conv_shortcuts=True)
x = identity_block(x, filters=512, kernel_size=(3, 3))
x = identity_block(x, filters=512, kernel_size=(3, 3))
x = AveragePooling2D(pool_size=(7, 7))(x)
x = Flatten()(x)
x = Dense(n_classes, activation='softmax')(x)
model = Model(inputs=input_layer, outputs=x)
return model
def ResNet50(input_shape=(224, 224, 3), n_classes=1000):
"""
:param input_shape:
:param n_classes:
:return:
"""
input_layer = Input(shape=input_shape)
x = ZeroPadding2D((3, 3))(input_layer)
# block1
x = Conv2D_BN(x, filters=64, kernel_size=(7, 7), strides=(2, 2), padding='valid')
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
# block2
x = bottleneck_block(x, filters=(64, 64, 256), strides=(1, 1), is_conv_shortcuts=True)
x = bottleneck_block(x, filters=(64, 64, 256))
x = bottleneck_block(x, filters=(64, 64, 256))
# block3
x = bottleneck_block(x, filters=(128, 128, 512), strides=(2, 2), is_conv_shortcuts=True)
x = bottleneck_block(x, filters=(128, 128, 512))
x = bottleneck_block(x, filters=(128, 128, 512))
x = bottleneck_block(x, filters=(128, 128, 512))
# block4
x = bottleneck_block(x, filters=(256, 256, 1024), strides=(2, 2), is_conv_shortcuts=True)
x = bottleneck_block(x, filters=(256, 256, 1024))
x = bottleneck_block(x, filters=(256, 256, 1024))
x = bottleneck_block(x, filters=(256, 256, 1024))
x = bottleneck_block(x, filters=(256, 256, 1024))
x = bottleneck_block(x, filters=(256, 256, 1024))
# block5
x = bottleneck_block(x, filters=(512, 512, 2048), strides=(2, 2), is_conv_shortcuts=True)
x = bottleneck_block(x, filters=(512, 512, 2048))
x = bottleneck_block(x, filters=(512, 512, 2048))
x = AveragePooling2D(pool_size=(7, 7))(x)
x = Flatten()(x)
x = Dense(n_classes, activation='softmax')(x)
model = Model(inputs=input_layer, outputs=x)
return model
if __name__ == '__main__':
resnet34 = ResNet34((224, 224, 3), n_classes=101)
resnet50 = ResNet50((224, 224, 3), n_classes=101)
print(resnet34.summary())
print(resnet50.summary())
标签：kernel,15,RESNET,strides,filters,图像,input,block,size From： https://www.cnblogs.com/liucaizhi/p/18233826